AIMC Topic: Cat Diseases
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Accelerometer-derived classifiers for early detection of degenerative joint disease in cats.
Veterinary journal (London, England : 1997)
Apr 9, 2025
Decreased mobility is a clinical sign of degenerative joint disease (DJD) in cats, which is highly prevalent, with 61 % of cats aged six years or older showing radiographic evidence of DJD. Radiographs can reveal morphological changes and assess join...
Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning.
Scientific reports
Feb 26, 2025
Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests ...
Machine learning predicts selected cat diseases using insurance data amid challenges in interpretability.
American journal of veterinary research
Feb 7, 2025
OBJECTIVE: To develop models for prediction of the onset of specific diseases in cats using pet insurance data and to evaluate their predictive performance.
Artificial intelligence-based quantification of lymphocytes in feline small intestinal biopsies.
Veterinary pathology
Oct 14, 2024
Feline chronic enteropathy is a poorly defined condition of older cats that encompasses chronic enteritis to low-grade intestinal lymphoma. The histological evaluation of lymphocyte numbers and distribution in small intestinal biopsies is crucial for...
Classification of the quality of canine and feline ventrodorsal and dorsoventral thoracic radiographs through machine learning.
Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Apr 26, 2024
Thoracic radiographs are an essential diagnostic tool in companion animal medicine and are frequently used as a part of routine workups in patients presenting for coughing, respiratory distress, cardiovascular diseases, and for staging of neoplasia. ...
Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program.
Journal of veterinary dentistry
Feb 6, 2024
There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiograph...
Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.
Scientific reports
Nov 9, 2023
Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features...
Deep learning-based diagnosis of feline hypertrophic cardiomyopathy.
PloS one
Feb 2, 2023
Feline hypertrophic cardiomyopathy (HCM) is a common heart disease affecting 10-15% of all cats. Cats with HCM exhibit breathing difficulties, lethargy, and heart murmur; furthermore, feline HCM can also result in sudden death. Among various methods ...
Further evaluation and validation of the VETSCAN IMAGYST: in-clinic feline and canine fecal parasite detection system integrated with a deep learning algorithm.
Parasites & vectors
Jan 29, 2021
BACKGROUND: Fecal examinations in pet cats and dogs are key components of routine veterinary practice; however, their accuracy is influenced by diagnostic methodologies and the experience level of personnel performing the tests. The VETSCAN IMAGYST s...
Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm.
Parasites & vectors
Jul 11, 2020
BACKGROUND: Fecal examination is an important component of routine companion animal wellness exams. Sensitivity and specificity of fecal examinations, however, are influenced by sample preparation methodologies and the level of training and experienc...
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